Language & Thought

 

1.    The Relationship Between Thought &Language

In terms of its consequences, one of the worst philosophical errors of the modern era is the implicit assumption that all thought is mediated by language: an error which it is all too easy to make due to the fact that most of the thoughts of which we aware are those which we either articulate or could articulate if asked to say what we were thinking. There are, however, times when we say something like ‘Wait! I’ve just had a thought’ and then take some time and effort to put that thought into words, strongly suggesting, therefore, that the thought preceded its articulation. What’s more, there are also times when, nagged by the feeling that we might not have got it quite right, we are then dissatisfied by the way we have actually expressed a thought, further suggesting that thoughts are, or at least can be, independent of language.

Of course, it may be argued that whatever subterranean cognitive processes go on prior to the articulation of a thought, these do not actually constitute ‘thinking’ in that the act of thinking actually consists in putting our thoughts into words. Even if one were to accept this as a definition of one form of thinking, however, it is very different from ‘thinking in words’ or using language to think, as when we construct a rational argument, for instance. What it does, in fact, is reveal three different levels in the relationship between thought and language: the pre-linguistic base level at which we have an unarticulated thought; the level at which we then struggle to make sense of this thought by putting it into words; and the level at which we then use language to examine, analyse and criticise the now publicly available ideas which, through their articulation, our thoughts have become.

For those who feel uncomfortable talking about thought in any way that hints at it being more primitive and basic than language, what we have done, however, is actually make the problem worse. For we now have two levels at which our cognitive processes our hidden from us: the base level at which we have a thought we haven’t yet expressed and cannot therefore identify or say from whence it came, and the almost equally opaque transformational interface between this base level and the fully articulated world of ideas, which T. S. Eliot famously described as a ‘raid on the inarticulate’ without thereby making it any more transparent.

Indeed, it is this lack of phenomenological transparency that is at the heart of all our problems when it comes to the relationship between thought and language. For it not only makes what’s going on at the subterranean levels of this relationship essentially unknowable but consequently precludes any further philosophical investigation of them. I say this for the very good reason that if something is unknowable, there’s not very much we can say about it. And as Wittgenstein stipulated at the end of the Tractatus: ‘Whereof one cannot speak, thereof one must be silent.’ The problem with this, however, is that if we eschew all talk of those aspects of the relationship between thought and language that are hidden from us and concentrate purely on the one aspect that is phenomenologically accessible, namely our use of language as a medium for thought, then we are in grave danger of forming a very distorted view of our relationship to language as a whole, which has some very unfortunate consequences.

One of the most glaring of these is that fact that if one fails to take it into account Eliot’s raid on the inarticulate, our creative use of a language would appear to be limited to the possibilities already inherent in it. That is to say that, while it may not be impossible to say something new, without being able to bend or repurpose words to new uses, typically through the use of metaphor – as I explained in ‘The Role and Importance of Metaphorical Truth’ – the use of any language is rigidly constrained by the current definition of its terms. Not only is this contrary to everything we know about the history of our intellectual development, however – to which I shall return later – but it also runs counter to Kant’s famous dictum in the ‘Critique of Judgement’ that true genius lies precisely in extending or developing a language so as to enable us to say something that could not have been said before.

Of course, it will be pointed out that no one is actually denying that we are able to bend language to our will so as articulate something that was previously beyond our grasp. Indeed, all that is being said is that we don’t know how we do this and so cannot really talk about it. There is, however, a huge difference between not talking about something and treating it as if it doesn’t exist. Even if we merely use it as a placeholder to fill in the blank created by its unknowability, moreover, there is a lot to be gained from acknowledging the existence of that about which we cannot speak if it consequently prevents us from making other philosophical errors, the most significant of which, in this case, is a tendency, reinforced by scientific materialism, to treat human beings as tropistic.

The best way to illustrate this is to use once again Dan Dennett’s example of the tropistic wasp, which he first introduced in a paper I heard him give at Birmingham University some forty-odd years ago and which I also used in ‘The Role and Importance of Metaphorical Truth’. The story goes like this. The female of this particular species of wasp excavates a nest into which she lays her eggs before going out to hunt for grasshoppers or locusts, which she stings and paralyses but does not kill so that they remain alive and fresh in order to provide food for her offspring during their larval stage. She then brings the paralysed grasshopper or locust back to the nest and leaves it on the threshold while she first checks inside to ensure that everything is as it should be. Satisfied that all is well, she then comes outside again, retrieves her prey and drags it into the nest.

On the surface, therefore, this not only seems like intelligent and purposeful behaviour but something akin to what we would regard as maternal. What entomologists discovered when they conducted further experiments on the wasp, however, was that if, during the time the mother spent checking the nest, they moved the paralysed locust a few inches away from the entrance, on coming back outside, she would drag her prey back to the entrance once again before going back inside to check the nest once more. If, while she was inside, they then moved the paralysed locust again, on coming back outside, she would once more repeat the process. And, as long they kept moving the locust, she would go on doing this over and over again, indefinitely.

Thus, what initially looked like intelligent behaviour is more like the product of a computer program, which, in this case, gets stuck in a loop. Professor Dennett’s point in presenting such a starkly clear example of tropistic behaviour, however, is the contention put forward by all materialist philosophers of the mind that, if one thinks of language as a kind of computer program – a fairly reasonable analogy – then while our own programming may be quantitatively more advanced and sophisticated than that of the tropistic wasp, it is qualitatively the same, in that both we and wasp are biological machines whose behaviour, it may be reasonably assumed, is entirely determined by our programming.

There is, however, a major difference between the two in that, whereas the wasp’s programming is entirely hardwired into its genes – as, indeed, is much of our own programming – our linguistic programming is acquired, much like software: a fact which, in itself, militates against the materialist position. This is because how we acquire this software, or learn a language, is as phenomenologically opaque to us as our ability to then alter or modify it to better express our thoughts. If we accept, therefore, that, even though we don’t know how we do it, we do, in fact, learn a language, there is no reason why we should not accept that we also have the ability to develop and extend that language so as to say something we could not have said before, even though we have no idea how we do this either.

In fact, the only thing stopping us from embracing this view of the relationship between thought and language is the fact that it also means embracing the idea that there are some things about the universe and even, indeed, about ourselves, that are unknowable but which must necessarily exist in order to explain things we do actually know, such as the fact that throughout our history we have continually found new ways to think and talk about the universe, as is particularly well demonstrated by the occurrence of paradigm shifts in science.

2.    Cultural Resistance to the Concept of Paradigm Shifts

Although I have written about this before, for those who haven’t read my previous essays on this and related subjects, the concept of a paradigm shift was first introduced by the American philosopher of science, Thomas Kuhn, in his 1962 book ‘The Structure of Scientific Revolutions’, in which he put forward a new model or paradigm of the way in which science, itself, develops. Instead of proceeding incrementally, as the traditional paradigm would have it, with one building block being laid upon another, Kuhn argued that science proceeds in stages, some of which are necessarily revolutionary. In fact, the usual lifecycle of any given field of science almost invariably starts with a revolution, when someone puts forward a new theory. As this gains acceptance, older theories are then abandoned and the science enters a stable phase. As time passes, however, some of the predictions which the new theory makes turn out to be false, requiring additional subsidiary theories to be developed in order to explain these exceptions. Over time, however, the number of exceptions increases, requiring more subsidiary theories to be developed, to which exceptions may also be found, requiring further subsidiary theories until the whole thing becomes so unwieldy that someone eventually says ‘Wait! I’ve just had a thought. What if we have been looking at this whole thing the wrong way round? What if we look at it like this instead?’ thereby introducing a new theory and starting the whole lifecycle all over again.

One of the best examples of this is Lavoisier’s creation of modern chemistry in the late 18^th century: one of the most remarkable contributions to science in all of history, which most people still do not understand. In fact, the general view is that Lavoisier discovered oxygen. But he did not. Oxygen was discovered by Joseph Priestly, who actually taught Lavoisier how to isolate it. It was just that Priestly didn’t call it oxygen. He called it dephlogisticated air. It was Lavoisier who called it oxygen, just as he called hydrogen ‘the maker of water’ when he discovered that, when he ignited it in the presence of oxygen, the two gasses combined to form H2O. What Lavoisier did, therefore, was not just discover a new element but create a whole new language for thinking about and describing the material world, in which the ancient concept of phlogiston, which had dominated chemistry for more than three hundred years, was discarded in favour of the concept of elementary particles, called atoms, which are combined in different ways and quantities to form different substances.

That’s not to say, of course, that he did it all on his own or in a vacuum. The term ‘atom’, for instance, had been introduced into modern science more than a century earlier by Robert Boyle, who derived it from the Greek word ‘atomos’, meaning ‘indivisible’, which was first used by the Greek philosopher Democritus in the 5^th century BC. What’s more, there was still a long way to go. Other elements and a whole host of new laws describing how they combine and act upon each other had yet to be discovered. It was Lavoisier, however, who created the basic model or conceptual framework upon which successive generations of chemists were consequently able to build, an achievement far greater than the mere discovery of a single element.

In fact, if more people understood what Lavoisier actually did, he would be regarded with far more esteem than he actually is, which rather begs the question as to why he is not. The answer, however, is really quite simple. It is because most scientists or, perhaps more accurately, the very institution of science, itself, if such there be, doesn’t like the idea of scientific revolutions, much preferring the traditional paradigm of science in which science proceeds in an incremental and orderly fashion and where every contribution, no matter how small, adds to the sum total of scientific knowledge. Despite all of the historical evidence to the contrary, therefore, from Copernicus to Einstein, wherever possible, the institution of science refuses to acknowledge that scientific revolutions and their concomitant paradigm shifts occur.

One of the reasons for this is the belief that the very idea of scientific revolutions undermines science. For if scientific revolutions have happened in the past, they can happen in the future, replacing current scientific paradigms with new ones that have yet to be conceived, thereby placing all current science under a provisional cloud. Even if it is conceded that scientific revolutions have happened in the past, therefore, it is generally agreed that they cannot happen in the future: an article of faith based on the implicit assumption that all of science’s current theories – especially its more foundational theories –are correct.

This, of course, is nonsense and is made demonstrably so by the application of Sir Karl Popper’s irrefutable argument that scientific theories cannot be proven, only falsified, which means that even if all our current theories were correct, we couldn’t know this. For even if a theory has so far survived three hundred years without being proven false, there is no guarantee that it will survive another three hundred years or even three hundred days. All scientific theories are therefore essentially provisional, which the existence of scientific revolutions only makes more uncomfortably clear.

There is, however, an even more profound reason why the institution of science doesn’t like the idea of scientific revolutions. For at the heart of every scientific revolution, of course, there is the creation of a new scientific paradigm, a new way of thinking about the world which requires precisely the kind of creative genius Kant describes: someone who is able to extend or reshape the language so as to say something that could not have been said before, someone, indeed, like Copernicus, Lavoisier or Einstein. The problem is that we do not know how these geniuses did what they did or, indeed, how anyone can rewrite a language so as to say something new. The experience is simply not accessible to us and being inaccessible, is therefore unknowable, which gives the institution of science yet another problem. For if something is unknowable, it is also, of course, unteachable.

In fact, in order to be teachable, a process or method has got to be completely transparent. Any institution attempting to teach science, therefore, must teach a scientific method that precludes the need for genius. Indeed, any predisposition or tendency among its students to think outside the box has got to be discouraged and a strict adherence to the prescribed method and current orthodoxy cultivated.

This is principally achieved by fostering a culture of both methodological exactitude and cooperation within the student body, such that students work together in a clearly defined and highly disciplined manner, rather than compete against each other by thinking along their own lines. This fostering of a strictly disciplined scientific mentality is also significantly helped by the fact that the first articulation of a new paradigm, like the first expression of any new idea, as Thomas Kuhn himself was all too well aware, is almost invariably inchoate: only half formed and full of holes and therefore very easily derided. Anyone putting forward such new ideas consequently has to work very hard to make any impression on the established order, especially when it is that established order that is handing out the jobs and research grants. The result is that institutional science is almost invariably conservative science, which has no place for revolutionary thinking. The problem with this, however, is not just that it suppresses something vital in the dynamic nature of science and leads to a kind of ossification, but that it also leads to science becoming corrupted, not just in the sense of financial corruption – though this too – but in the sense that it is no longer scientific.

3.    The Corruption of Science

In fact, it is the corruption of science, itself, that actually leads to financial corruption and therefore comes first. And it does so primarily by inverting the relationship between empirical data and theory, such that empirical data no longer has primacy.

To understand how this happens, we need to go back to the most basic model of science in which theories are created to explain empirical observations. The theories are then tested by making predictions based on these theories and conducting experiments to find out whether the predictions are accurate. If the predictions are accurate, then there is a chance that the theory is correct, though only a chance. For it is perfectly possible that experiments conducted to test other predictions based on a theory may prove the predictions false, which may prove the entire theory false. On the other hand, we may attempt to explain these exceptions by either modifying the original theory or by creating subsidiary theories in the way described above. Indeed, in Kuhn’s description of the standard lifecycle of a scientific theory, it is only when the number of subsidiary theories gets out of hand, leaving us with more patches than original fabric, that we are eventually forced to abandon the theory altogether.

In today’s institutionalised science, however, the decision to abandon a theory altogether is even more difficult. For that would be to admit that for years, perhaps, the institution has been teaching something that it now regards as false. And this is something that it is very difficult for any institution to do. After all, people’s careers and reputations are at stake. What’s more, with revolutionary thinking having been institutionally suppressed in science for at least the last two generations, the chances of there being an alternative theory or paradigm available to replace the one that now needs to be abandoned are very slim. No matter how much empirical evidence has built up to prove a theory false, therefore, it is the errant data that must now always be explained away rather than the theory abandoned, which is to say that it is the theory, rather than the data, that now has primacy.

Once the primacy of theory has been established in a culture, this then has two further consequences. The first of these is that scientists no longer feel quite so constrained by the sanctity of data. If the data does not conform to the theory, therefore, they are now far more inclined to either discard or change it than was previously the case. Nor is this necessarily cynical. After all, if one sincerely believes in the theory one is putting forward or defending, errant data must surely be faulty data. Once selecting or modifying data to fit one’s chosen theory has become commonplace within an institution, however, it becomes very easy to start doing it, not because one particularly believes in the theory, but simply in order to get the results one needs in order to maintain one’s research funding and keep one’s job.

In fact, there is a considerable amount of evidence to suggest that such corruption is now endemic throughout most scientific institutions. The editor of one journal I quoted in a previous essay on this subject actually believed that up to 20% of all the papers submitted to his journal for publication were not just based on selected or modified data but on no data at all, it all having been made up. What is even more disturbing, however, is the fact that, believing in their theories rather than the sanctity of data, many scientists do not seem to think that there is anything wrong with this, a development in the very culture of science which has been further encouraged by the use of computer models which, themselves, have little in the way of empirical grounding.

Indeed, most computer models start with a theory which is turned into a set of algorithms, which it then uses to predict future observations and measurements under different conditions. Modifications are then iteratively made to the algorithms to improve their predictive accuracy, although this in itself can be very problematic. For while modifying an algorithm to make its predictions conform to reality may seem very similar to the development of subsidiary theories which Kuhn describes, in traditional science these patches developed to explain exceptions to a main theory had to have some theoretical basis. Simply modifying an algorithm to make its predictions fit the facts, on the other hand, can leave us in a position in which the model’s predictions are now correct but we have no idea why. Worse still, many large scale computer models are subject to continual development over many years, to which a lot of people may contribute, especially in a university setting, with the result that there comes a point at which it’s possible that no one single person actually knows how the model works. It becomes a magical black box which issues oracular prophecies without anyone knowing how it does so. And yet, believing in this magical black box, we still believe in the prophesies, even when they don’t come true.

One of the best examples of this is the Coupled Model Intercomparison Project (CMIP), in which 102 institutions from around the world were originally funded to predict future changes in the world’s climate based on two key assumptions: that such changes are primarily driven by the accumulation of carbon dioxide in the atmosphere and that, without a modification in our own behaviour, this accumulation will continue at a rate of 1% per year.

On this basis, the project’s first set of predictions were published in 1995 and covered the next twenty year period leading up to 2015. In fact, it was this first set of CMIP predictions that led Al Gore to predict that summer arctic sea ice would have disappeared by 2014. By the time 2014 arrived, however, it was perfectly obvious that the predictions of all 102 institutions taking part in the project were wildly inaccurate, with some of them being out by more than 1°C when compared with actual data from satellites and weather balloons, the two most reliable sources of such data we have. And yet it is the predictions of these models that the world continues to believe.

4.    An Absence of Critical Thinking

So how is this possible? In previous essays on this subject, I have put forward two possible answers. The first is that there are just too few people in the world who actually know the science, leaving the rest of us to just take their word for it. The problem with this, however, is that there are some people who know the science and one would expect at least some of them to say, ‘Hold on  minute, this isn’t right’. In fact, I have actually based some of my own essays on the work of two such upstanding scientists: Richard Lindzen, Emeritus Professor of Meteorology at MIT, and William Happer, Emeritus Professor of Physics at Princeton University.

This then led me to my second answer: that there is a much larger contingent of scientists who have a vested interest in the theory of anthropogenic global warming than those who are simply committed to honest science with the result that it is the former group whose voices are always heard. The problem with this, however, is that it would seem to entail what would have to be the biggest conspiracy in history. For it is not just scientists who would be required to continually espouse something they did not believe, but everyone who comes into contact with any aspect of reality upon which global warming should be having an effect. Anyone working in the arctic, for instance, would have surely noticed that this summer, ten years on from when Al Gore said it would all be gone, arctic sea ice was as extensive and as thick as it has been throughout the last century. While corruption and our predisposition to uncritically accept the authority of experts may both have contributed to inducing our current state of mass delusion, therefore, there is clearly something else going on here, which raises the possibility, indeed, that it is actually our current state of mass delusion, itself, that is that something.

After all, what is a mass delusion other than a highly prevalent way of thinking that is not actually supported by real world evidence, of which there have been hundreds if not thousands of examples  throughout our history. Indeed, it could be said that our entire history is a history of such delusions. We acquire them, they dominate our way of thinking for a while, and then a Copernicus, Lavoisier or Einstein comes along and says something that eventually makes us see the world in a completely different way. The problem, of course, is that it is never quite that easy. For not only are we resistant to such revolutionary changes in our world view but are so of necessity, in that language could not exist if we changed our way of thinking every other minute. In fact, language actually needs periods of stability in which everyone uses the language in the same way in order for us to explore the logical the ramifications of the prevailing paradigm, thereby revealing its flaws and paving the way for the next revolution.

The problem is that, sometimes, our resistance to the next revolution is so strong that it effectively blocks it, sometimes for centuries. No matter how much evidence accumulates showing that the old way of thinking is wrong, those who have a vested interest in perpetuating it continually prevent change from occurring, sometimes even going so far as to systematically kill the proponents of change.

Fortunately, we are not actually doing that at the moment. Because we do not understand the process by which one way of thinking replaces another, however, and refuse to accept that it even occurs, we have now become so trapped in our current way of thinking that we cannot get out of it even though its flaws are not just glaringly obvious but are starting to cause us real world problems.

Take, for instance, the quest to achieve Net Zero carbon emissions with which the west is currently obsessed and which is largely focused on two main goals: the decarbonisation of our electricity grids and the replacement of petrol and diesel engined vehicles with purely electric vehicles. If the objective of these goals is as stated, however, not only is this focus far too narrow, omitting such forms of transport as airlines and cargo ships, both of which have massive carbon footprints, but the goals themselves are incompatible, as any critical analysis very quickly reveals.

In fact the problem is almost immediately apparent as soon as one considers that in 2023, wind and solar power constituted just 34.3% of the UK’s total electricity generation. When conditions were optimal, there were indeed periods during which they actually contributed more than this; but this was their average contribution across the year. If the UK is going to completely decarbonise its electricity grid by 2050, this means, therefore, that it is going to have triple its wind and solar generating capacity over the next 25 years, an objective which, under any conditions, would be very challenging. If, at the same time, however, we are going to replace all of the 41.2 million petrol and diesel engined vehicles on our roads with EVs, we are going to have to increase electricity production by another 37.5%, which effectively means quadrupling our wind and solar generating capacity over this period. Even putting aside the cost, therefore, which, given the current state of our finances, presents yet another challenge, it is very doubtful whether this is even remotely feasible.

If our goal were solely to decarbonise our electricity grid, while keeping petrol and diesel engined vehicles on our roads, we might be able to manage it. Similarly, if our objective were solely to replace all petrol and diesel engined vehicles with EVs while continuing to power our electricity grid with natural gas, this too might be possible. Trying to do both at the same time, however, is something which only a religious zealot who hasn’t actually thought about it would even consider.

What’s more, this doesn’t take into account the very real possibility that running an electricity grid purely on wind and solar power is actually impossible. I say this because, being dependent on the weather and hence intermittent in their electricity generation, an entirely wind and solar powered grid would have to have some form of battery storage back up for when the wind doesn’t blow and the sun doesn’t shine. This, however, is far more expensive and difficult to achieve than advocates of an entirely decarbonised grid would appear to think. In my 2021 essay on this subject, for instance, I calculated that, using the Tesla Powerpack 2 4HR battery system, at that time the leading large scale battery storage system on the market, it would cost £550 billion just to store one day’s output from our then wind and solar capacity of around 32 GW.

This being clearly non-viable, people are now therefore talking about using ‘green’ hydrogen as our storage medium, the idea being that the hydrogen would be produced by electrolysis using wind and solar generated electricity on days when the wind does blow and the sun does shine and then burnt in modified gas fired power stations when an alternative source of energy is required. What this doesn’t take into account, however, is how much more electricity one would have to generate in order to produce the hydrogen, or the fact that it takes 50% more energy to produce hydrogen by electrolysis than one actually recovers by burning it. As a solution to the storage problem, therefore, this would only make sense if ‘free’ wind and solar power really were as cheap as people like to believe, thereby making their profligate use to produce hydrogen economically viable. The fact is, however, that wind and solar farms are only ‘viable’, themselves, if they are heavily subsidized. For as I have demonstrated elsewhere, they too consume more energy in their manufacture, installation, operation and maintenance than they ever produce in their lifetime, making the whole renewables industry an exercise in economic futility and corruption.

The entire Net Zero project is therefore a total fantasy, unsupported by either economic or engineering reality. Not only can it not be achieved, however, but if we continue pursuing it, it could easily result in a disaster. For not only is it inevitable that, if forced down this road, the electricity grid would eventually fail, along with all the computer systems that depend on it, but it is also fairly certain that this would be very shortly followed by the failure of everything that depends on a computer, which, in today’s world, is just about everything. With no power to heat our homes and no food in the shops, societal collapse would then shortly follow, with riots in the streets, widespread looting and the complete breakdown of law and order.

Not, of course, that it will actually come to this. For what can’t be done, won’t be done. The only question is how much damage will be done before the reality of the situation finally sinks in. For no matter how much evidence accumulates demonstrating that the whole Net Zero project is a fool’s errand, there will be those who will sill resist abandoning it. Nor will these diehard advocates of Net Zero be confined to those with a vested interest in having it continue, including those paid by government to advise them on the subject, who will no doubt insist to the very end that it can be made to work. An even louder voice will almost certainly come from those who believe that there is no alternative, the alternative being that the planet is destroyed. After all, 95% of scientists agree that the accumulation of carbon dioxide in the atmosphere is the most significant cause of global warming and that we, ourselves, are the most significant cause of this accumulation. What’s more, based on our traditional view of science, combined with our current inversion of the roles of theory and data within it, this is not just regarded as a theory but as a matter of fact.

Nor does it help when faced with such a mind-set to point out that, before Lavoisier put forward a much better theory – one which more accurately predicted what happens in the real world – 95% of scientists believed that all non-metallic materials lost weight when heated because they gave off phlogiston. For in order to see these two situations as analogous one actually has to subscribe to Thomas Kuhn’s paradigm of the way in which science proceeds, which we, of course, do not. Indeed, one could say that this was our real or underlying problem if there weren’t something even more fundamental still yet underlying it. For our problem is not just our choice of paradigm for describing and understanding the activity we call science, but our entire view of the universe. For believing in an entirely material universe, entirely knowable and explicable by science, we are unable to believe that anyone could do what Thomas Kuhn claims Lavoisier did: rewrite his own linguistic programming so as to think and say something he was not able to think or say before, a feat which is not only phenomenologically inaccessible to those who are able to do it but completely inexplicable in materialist terms.

For those of us who are completely wedded to the materialist worldview, therefore – which is just about everybody in the modern world – giving up the traditional paradigm of science and adopting Thomas Kuhn’s is simply unthinkable. It would be akin to an atheist converting to Christianity and world require something just as revolutionary to cause it. Unless we undertake this philosophical journey, however, and accept that there are aspects of the universe that are fundamentally unknowable, including ourselves, we shall remain as trapped in our closed way of thinking as Dan Dennett’s tropistic wasp until our failure to see its flaws eventually destroys us.

 

Problems in the Culture of Modern Science

 

I first began to suspect that there might be something wrong with the culture of modern science in the mid-1980s, when I taught at the University of Jyväskylä in Finland and sought to augment my lowly junior lecturer’s salary by editing the English language versions of scientific papers being submitted for publication by my Finnish colleagues. As most Finns speak excellent English, this task largely consisted in tidying up the grammar a little, mostly by cutting out a number of commonplace ‘Finnishisms’ which arise in Finnish English as a result of the structural differences between the two languages, with Finnish, for instance, using case endings instead of prepositions, having only one word for the third person singular instead of three, and being entirely without a verb ‘to have’. In order to satisfy myself that any given sentence or paragraph made sense, however – and, just as importantly, actually expressed what I thought the author intended – I nevertheless found that I usually had to spend quite a lot of time learning the language of the science, itself, which primarily consists in the nouns and verbs which denote a science’s objects and the ways in which these objects relate to or act upon each other.

Given how much time I usually had to spend on this background research, I was always especially pleased, therefore, whenever I received repeat business within the same scientific field, in that while I could still charge my regular fee, I didn’t have to learn a whole new scientific language from scratch. It was when dealing with one particular repeat customer, however – an ophthalmologist – that I began to notice something odd about the way in which he set about communicating the results of his work to the rest of the world. For the empirical study upon which he based the second paper he brought me appeared to be exactly the same as the study he had used for the first paper I had edited for him. In fact, for a while, it even made me wonder whether I’d picked up the wrong document, especially when I found myself correcting sentences I was fairly sure I’d corrected before.

For a while, indeed, it even made me feel slightly annoyed. For once I’d checked that the second document was, in fact, a different paper – in that it had a different title, at least – I felt that the minimum the author could have done was incorporate my previous corrections into the new work. By not doing so, it was almost as if he didn’t care about the improvements I’d made to the previous paper’s presentation, in which I took some pride, trivial though my contribution may have been to the work as a whole. Because so much of the second paper was more or less identical to the first, however, I refrained from saying anything, happy to hand my client a substantial invoice for what had essentially been a few hours work copying my previous corrections from one document into the other.

Then he brought me a third paper and, lo and behold, it was based on exactly the same empirical study as the previous two. The methodology was the same, the test subjects were the same, and so were the results as set out in the various tables. The only differences between the three papers I could discern were a number of additional findings and conclusions set out towards the end of each one, which, in my naivety, I thought could have been presented better had they all been in the same paper: something I was foolish enough to suggest to the author when he came to pick up my final set of revisions.

It was one of those rare moments in life when the scales fall from one’s eyes and one suddenly sees the world for what it truly is rather than what one had childishly supposed it to be. For instead of thanking me for such an insightful suggestion, my client simply looked at me as if I were a complete idiot. And then the penny dropped. For these ophthalmological papers, over which I had diligently laboured for many more evenings than I now suspected they were worth, were not about imparting new information to fellow scientists working in the field. Even less were they about adding to the total sum of human knowledge. They were simply about adding three more titles to the list of publications on the author’s curriculum vitae in order to advance his career, either by helping him to secure further funding for his research and by enabling him to obtain a better position within the university system: two measures of career success which were already connected back in the 1980s, but are now almost inseparable.

I say this because most university graduates today leave university with a far greater level of debt than they did forty years ago, such that, in most cases, they cannot even contemplate post-graduate research unless their tuition fees are paid and some level of maintenance support provided. Because most western countries regard their science base as essential to their economic prosperity – and therefore require a large pool of science graduates to undertake post-graduate training – most countries, especially in Europe, have consequently developed public funding regimes for both scientific research and post-graduate support which basically tie the two together, such that if a student wins a place on a publicly funded research programme, their own funding automatically comes with it, their eligibility for funding actually being determined by their selection for the programme.

What this also means, however, is that if a university science department wishes to run a post-graduate programme, as most university science departments do – such programmes figuring prominently in all their marketing material – then funding for some area of research must first be secured, with the further consequence that those members of the department who are particularly good at this tend to be more highly regarded and enjoy a higher status than those who aren’t. What’s more, this then affects recruitment. For in recruiting members of a science department, particularly the department head, most universities tend to place far more emphasis on a candidate’s track record in obtaining research funding than on their teaching ability, thus making this the key attribute in building a successful academic career.

For universities and scientists alike, however, this whole system of funding has put them on something of a funding treadmill. For if universities want to continue their prestigious research and post-graduate programmes, and if scientists wish to maintain their high status positions, then they have to continually win more funding, constantly going back to the relevant funding agencies in a never ending cycle of application and political lobbying which can – and often does – have the effect of inverting the relationship between funding and research, such that instead of obtaining funding in order to continue their research, scientists now too often find themselves in the position of undertaking research in order to continue obtaining their funding, which, in turn, then places them on another treadmill: that of having to continually produce results – in the form of a never-ending stream of published papers – in order to justify all the money they have been given and thus be given more.

Quite predictably, this has also brought about a massive expansion in the scientific publishing industry. For in order to accommodate the demands of so many scientists who have to get their results published in order to justify their funding, dozens of new scientific journals are founded every year. Today, indeed, it is estimated that there are more than 30,000 such journals worldwide, which collectively published over 2.2 million scientific papers in 2018, the latest year for which I have figures, most of which, statistically, can only have achieved a very limited readership. Indeed, there is a widespread joke within the scientific community – indicative of an equally widespread cynicism – that the vast majority of all published papers only ever have three readers: the authors, themselves, the editor of the publishing journal, and the referee appointed to conduct peer review.

Amusing as this may be, what is far less amusing, however, is the ever-growing cost of this publishing juggernaut, which, like the cost of scientific research, itself, is either met by students – in the form of tuition fees – or by taxpayers – either through grants to universities or through research funding – these two sources of finance roughly corresponding to the two principal ways in which scientific journals make their money.

The first and more traditional of these is by charging annual subscriptions, both for online access to individual papers and for the annual bound editions which still accumulate on many university library shelves. Given the level of growth in the industry, however, it was inevitable that at some point the cost to universities of further expansion based on this model would simply have become prohibitive. Most new journals – those which have been founded during the last couple of decades or so – have therefore adopted an Open Access (OA) model, in which the published papers are free to read online, with the costs being covered by the authors. The idea that this makes them ‘free’, however, is something of a wilful misconception. For knowing that they are going to have pay to have their research published in this way, most scientists now include the cost of publishing in their applications for research funding, with the result that the costs are simply charged to the public purse.

Indeed, it is the fact that no one who cannot pass the cost of these journals on is ever directly charged for them that permits their seemingly infinite expansion. For if the only payments journals received came from those who actually consumed their contents, and if these consumers only paid for what they actually consumed – whether this be in the form of single online papers or the quarterly paperback editions to which I, myself, used to subscribe – then not only would there be far less money flowing into the scientific publishing industry but, as a consequence, there would also have to be far fewer journals, which, in turn, would mean that far fewer papers were published.

The problems to which this lack of any commercial restraint give rise, however, are not confined merely to this ever-increasing drain on public funds. For by providing what is, in effect, a virtually limitless publishing capacity, publishers have not only ceased to perform the filtering function they once provided – ensuring, instead, that just about every scientist can now get their work published, no matter how mediocre or entirely meritless it may be – but have also largely abdicated their responsibility for maintaining standards, thereby allowing scientists to increasingly get away with some fairly unscientific practices.

Probably the most widespread of these is something called p-hacking, which is the selective reporting of results in order to support a theory which the raw data as a whole would not support. Another common form of data manipulation is something called HARKing, or Hypothesizing After the Results are Known, which may sound fairly innocuous, but which actually means that the hypothesis in question, coming the at end of the process rather than constituting its starting point, is never really subjected to any serious scrutiny.

What both of these practices thus do is allow scientists to publish results which they wouldn’t otherwise be able to obtain, thereby making the incentive for going down this road fairly obvious. For if one needs to publish something within a particular time frame in order to maintain one’s funding, but hasn’t yet discovered anything of note in one’s current line of research, then the temptation to resort to some sort of statistical sleight of hand may well be overwhelming, especially given the fact that, because both of these practices involve some fairly complex statistical techniques, they can also be fairly hard to detect without detailed analysis of both the data and methodology employed. 

Indeed, the sophistication of some of these techniques, along with the fact that their application – at the desk-top level – has only recently been made possible by increases in personal computing power, has led some commentators to speculate that, in some cases, the abuse of these techniques may well have been unwitting, the suggestion being that in exploring the potential of new analytical tools, some scientists may have crossed the line inadvertently. And, in some cases, it’s perfectly possible that this is how these practices began. It is also perfectly possible, however, that having discovered these easy and convenient ways of obtaining results, many scientists then talked themselves into believing that while technically unscientific and invalid, they weren’t really doing any harm, especially as no one was likely to read the resulting paper anyway.

The problem, of course, is that once one has created an environment in which the need for scientific integrity is no longer felt to be absolute, it opens the door to other, far more obviously intentional and less ‘innocent’ forms of abuse, the simplest and most blatant of which is that to which Charles Rotter, editor of the journal ‘Molecular Brain’, drew the public’s attention earlier this year, in an article in his own journal in which he describes how, of the 180 papers submitted to the journal during the previous two years, he had to recommend that 41 of them be ‘revised before review’, his principal request being that the authors submit their raw data for appraisal. He then reports that:

‘among those 41 manuscripts, 21 were withdrawn without providing raw data, indicating that requiring raw data drove away more than half of the manuscripts. I rejected 19 out of the remaining 20 manuscripts because of insufficient raw data. Thus, more than 97% of the 41 manuscripts did not present the raw data supporting their results when requested by an editor, suggesting a possibility that the raw data did not exist from the beginning, at least in some portion of these cases.’

In that 40 withdrawn or rejected manuscripts out of 180 represents 22% of all the manuscripts submitted to ‘Molecular Brain’ during this two year period, should this be happening at all of the  30,000 scientific journals currently published around the world, this would mean that up to 620,000 papers could be being submitted to and rejected by journals each year on the grounds that they are entirely spurious. The suspicion, however, is that they are not all rejected. For not only is it to be doubted whether all journal editors are quite as scrupulous as Charles Rotter, it is also hard to believe that so many scientists would attempt this kind of scam unless they thought they could get away with it, which suggests that, at least some of the time, they do.

The question, of course, is how widespread these various types of scientific fraud are. And the answer, unfortunately, is that it is almost impossible to tell. For if all attempts at fraud were known, then, presumably, they would all be stopped. One clear indication of their increase, however, is the growing problem of scientific irreproducibility, wherein other scientists are not able to reproduce the results reported in a scientific paper following the methodology laid out in the paper itself: the clearest possible sign that there is something wrong with the underlying science.

Again, it is difficult to estimate the overall extent of the problem in that it varies from field to field. What should not come as a surprise, however – but is shocking nevertheless – are the fields in which it would appear to be most prevalent. For they are not the fields which common prejudice would lead one to expect – such as those in the social sciences, for instance – but rather those which are not only the most competitive, being awash with money, but in which we naively expect a higher level of integrity to be maintained, the most notable being cancer research. Yet according to one meta-analysis by F. Prinz et al, published in 2011, only around 20% to 25% of published studies in cancer research could be validated or reproduced, while another analysis by Begley and Ellis in 2012 put the figure as low as 11%.

If you find these figures as shocking as I did when I first stumbled upon them, then, like me, you will also probably be asking how such a systemic failure to maintain scientific integrity is possible. For surely there has got to be someone who checks the validity of a scientific paper before it is published: if not the journal’s editor, then whoever is appointed to conduct peer review. What you have to remember, however, is not just that referees are traditionally unpaid – their lack of reward or inducement supposedly ensuring their impartiality – but that the current system of peer review was instituted at a time when science was still largely a gentlemanly pursuit, when the only form of income universities brought in came from teaching, and when scientists largely conducted their research in between the lectures and tutorials for which they received their salaries. By not receiving any remuneration for their research as such, not only were they thus under no pressure to publish their results until they were ready – or, indeed, until they had something worthwhile publishing – but they also had absolutely no reason to commit fraud, which, in turn, meant that their colleagues were quite happy to act as unpaid referees, secure in the knowledge that all this would actually entail was a hopefully enlightening read and a quick check for obvious errors that might otherwise embarrass the author and journal alike.

Of course, now that the situation has changed, publishers could start paying their reviewers and demanding a more rigorous appraisal from them. Not only would this lead to the rejection of more papers, however – for which the publishers would still have to pay the reviewer’s fees – but even if reviewers only spent an extra couple days going through an author’s data and methodology, the additional cost could be the final straw for those who have to foot the bill, thereby pushing the whole industry over the edge.

More to the point, a lack of critical depth and rigour in the reviews carried out by referees is not the only problem with the current system of peer review. There are also problems of both scale and anonymity. For 2.2 million scientific papers published every year do not just require 2.2 million referees, but 2.2 million suitably qualified and impartial referees, who, in principle, should not know the authors of the papers they are reviewing: something which it is hard enough to achieve even in the most heavily populated areas of science, but is made even more difficult by the fact that, in order to stand out in any given field, most scientists today quite naturally gravitate towards some sort of niche specialism, in which they can make a name for themselves as one of only a handful of experts. What this also means, however, is that, sometimes, it can actually be difficult for a publisher to find a suitably qualified referee at all, let alone one who does not know the author of the paper to be reviewed. For niche specialisms create niche worlds, in which the participants regularly attend and speak at the same conferences, making anonymity almost impossible.

Worse still, publishers are in the business of publishing. If within a certain niche specialism there are rivals and competitors, the last thing they want to do, therefore, is send out a paper to be reviewed by a referee they know is going to be hostile. The inevitable result is that networks of like-minded scientists are formed, who are known to be sympathetic to each other’s ideas and who, through the intermediation of a common publisher, regularly review each other’s papers, bringing the whole concept of impartial and independent peer review into question.

What makes this whole system even more insidious, however, is the fact that, even when they are acting corruptly, it is extremely unlikely that those doing so actually see themselves as corrupt. Whether they are manipulating data in order to get the results they want or rubber-stamping the paper of a colleague whose views they share, the likelihood is that they simply see it as normal: as the way in which science is conducted these days. For this is not the corruption of individual scientists; it is the corruption of the culture of science as a whole, which makes it all the more difficult to reform. For if wrong-doers do not believe that they are doing anything wrong, it is very hard to get them to change their ways, especially if changing their ways would be to their detriment and might eventually involve them in trying to amend the ways of others, thereby earning for themselves a reputation for being  trouble-makers and further disadvantaging their careers.

Indeed, once corruption of this type has taken hold of an institution, it is almost impossible to eradicate it, especially when the institution in question is almost completely opaque to outsiders: a condition which, in itself, tends to foster corruption. For having only a limited understanding of how the institution works, those looking in from the outside are not only rendered more or less incapable of imposing reform from without, but can be far more easily manipulated into giving the institution their support. What’s more, due to this general level of ignorance, any support given, will be largely under the control of the institution itself. For in order to determine how this support should be allocated, those providing it have very little choice but to co-opt or seek the advice of institution members, thereby opening the door to even greater corruption.

Not that the real-world relationship between science and government is actually quite this one-sided. For as in any relationship of patronage, the patron always has a great deal of leverage over the patronised, especially where the patronage is largely financial in nature and where those receiving it are entirely dependent upon it, as is the case with respect to nearly all non-commercial science throughout the west. While scientists may still have a lot influence, especially in advising government as to where their money should be spent, the need to keep the funding flowing creates a far more symbiotic relationship, in which scientists are not only obliged to subordinate many of their own scientifically prompted aims to the government’s more strategic agenda, but have frequently shown an enthusiastic willingness to be of service to government in ways that are not always particularly good for them and which have further corrupting effects.

One of the most pernicious of these has been the tendency, in recent years, for scientists to push science beyond its traditional role of merely explaining how the universe works and to turn it, instead, into a tool for predicting the future. This they have done through the almost ubiquitous application of computer models or simulations, the increased influence of which, especially in the development of government policy, has been allowed to go unchecked largely because neither governments nor the public, nor most scientists, themselves, fully understand either the proper place of such models or the potentially catastrophic effects of their misuse.

To understand these dangers, however, one must first understand what computer simulation actually are and how their application differs from the application of standard scientific method. And one of the best ways I have found of explaining this difference is to start by comparing what might be called the relative ‘directionality’ of the two approaches. For all methodologically rooted disciplines follow what is largely a step by step process which has, as a consequence, a directional flow. In the case of standard scientific method, this process starts with observations and measurements which are then analysed to reveal patterns or anomalies. Hypotheses are then formulated to explain these patterns or anomalies and experiments designed to test the hypotheses. Those hypotheses which do not fall at the first hurdle but need some modification are then refined on an iterative basis, with further experiments designed to test each refinement, until a stable theory is finally reached.

The process of building a computer model, however, is very different. In fact, it flows in completely the opposite direction, in that it actually starts with a theory. It then turns this theory into a set of algorithms, which it then uses to predict future observations and measurements under different conditions. Modification are then iteratively made to the algorithms to improve predictive accuracy.

This reversal of directionality is absolutely crucial. For it means that the soundness of any computer model largely depends on the soundness of the underlying theory, the old adage of ‘rubbish in, rubbish out’ saying it all. As a result, the most reliable computer models are usually to be found in areas of applied science in which the underlying theories and principles have long been established. Good examples include fields like fluid dynamics, of which I have some personal if second-hand knowledge, having once had a friend who gained his Ph.D. in mathematics modelling turbulence in gas pipes. Then, of course, there are the long-standing applications of computer modelling in both engineering and construction, where it is an accepted principle that it is far better to run a computer simulation to find out whether a building, built to a certain design, will withstand an earthquake of a specified magnitude, than it is to actually build it and find out the hard way.

Even here, however, one must sound a note of caution. For even when basing one’s computer model on a theory of long standing, one never knows when exceptions may be discovered. This is because scientific theories are essentially constructs of the imagination designed to explain the inner workings of the observable universe and are not, themselves, observable. What this means, therefore, as Sir Karl Popper explained in ‘The Logic of Scientific Discovery’, is that no scientific theory can ever actually be proven. For no matter how well established a theory may be, there is always the possibility that someone, someday, will discover some new piece of evidence inconsistent with its possible truth, thereby proving it false. Indeed, as Popper also famously argued, the possibility of falsification, or falsifiability, is actually a condition of a theory being scientific, in that if it cannot be falsified or one cannot say what piece of empirical evidence would prove it false, then it is simply not a scientific theory. 

What’s more, history shows us that even theories which have been held true for centuries and which have had, in their time, a high level of predictive accuracy, can eventually be proven false. A prime example of this is Sir Isaac Newton’s theory of gravity, in which gravity was conceived as an attractive force pulling bodies together, a bit like magnetism. Indeed, many people still think of it in this way today. More to the point, the mathematics based on this way of conceiving of gravity accurately predicted most of the observable universe for more than two hundred years. In fact, the only observation it did not correctly predict was the orbit of the planet Mercury, which remained an unexplained anomaly until the beginning of the 20^th century, when Albert Einstein produced a new mathematical formula based on a completely different concept: one in which gravity was now conceived, not as an attractive force, but as a warping of space due to the mass of the bodies within it, the calculated effects of which – based on relative mass and proximity – far more accurately approximate the orbit of the tiny planet Mercury around its massively larger star.

That the mathematics derived from Newton’s concept can still be used to accurately predict the movements of the other planets has, of course, led some people to mistakenly assume that, in these unexceptional cases, Newton’s theory still holds true. However, this is clearly a misconception. For either Newton’s theory is correct in every instance, or Einstein’s is. And since Newton’s theory has been found to be false in at least one instance, the betting is on Einstein, though even here, of course, our acceptance of Einstein’s theory is still only provisional. For, in time, it too may be proven false. For such is the nature of science.

To avoid this kind of confusion, however, it may be helpful to consider yet another, even clearer example of a long-term theory being overturned and replaced by a new theory, the old theory, in this case, being one which no one, today, would mistakenly believe was still true. For the theory I have in mind is that which was the foundation of what is now generally known as phlogistic chemistry, which held that those forms of matter which lose mass when heated do so because they give off a substance called phlogiston.

Because, today, we now know – or think we know – that no such substance exists, this, of course, seems laughable. But phlogistic chemistry was actually very successful in its time and lasted for more than three hundred years, which is significantly longer than modern chemistry has so far survived. Indeed, the only thing that phlogistic chemistry could not explain was why certain forms of matter, i.e. metals, gained mass when heated, which we now know – or think we know – is the result of oxidation, making it somewhat inevitable, therefore, that it was the discovery of oxygen, by the French chemist Antoine Lavoisier, which eventually brought phlogistic chemistry to an end.

Or, at least, this is the account given in most potted histories of science. It is, however, a total misrepresentation of what actually happened. For in that the gas we now know as oxygen was first isolated by the English scientist Joseph Priestley, it is questionable whether Lavoisier can be said to have discovered it at all, especially as he was actually shown how to isolate the gas by Priestley, himself, at a gathering of French scientists in Paris in 1774. It was just that Priestley did not call it ‘oxygen’. He called it ‘dephlogisticated air’ and continued to practice the science of phlogistic chemistry for decades after Lavoisier coined the new name.

So why is Lavoisier credited with the discovery, and what did he actually do other than give Priestley’s dephlogisticated air a new name which ultimately proved to be just as mistaken? I say this because the word ‘oxygen’ is derived from the Greek words oxys, meaning ‘sharp’, and genes, meaning ‘to create’, and was chosen by Lavoisier because he thought the gas had an important role in the formation of acids, which turned out to be false. In fact, had Lavoisier ended his study of ‘oxygen’ at this point, it is likely that his name would have gone down as a little more than a footnote in history and that we’d have ended up calling the second most abundant constituent of the earth’s atmosphere something else entirely. It was while he was studying some of the other properties of this incorrectly named gas, however, especially its role in combustion and the roasting of metals to produce powders or calxes, that he had his ‘eureka’ moment. For noting – as others had done before him – that the powders which resulted from the calcination process were heavier than the metals with which the process started, and reasoning that it could not be the application of heat alone which caused this weight gain, since ‘heat’, itself, had no mass, he hypothesised that the calcined metals had to be drawing something else out of the atmosphere, with Priestley’s gas, now his own ‘oxygen’, being the prime contender: a hypothesis which was then given even more traction when he learned how to isolate yet another new gas, one which was isolated for the first time in 1776 by yet another English scientist, Henry Cavendish. For in studying the properties of this second new gas, he discovered, quite remarkably, that, on combustion, small amounts of water were produced, suggesting that, as in the case of the calcined metals, combustion actually caused this second gas – which he duly called ‘hydrogen’, or ‘creator of water’ – to combine with something else, with oxygen, again, being the most likely suspect.

Not, of course, that he had any way of proving this. For I repeat once again that scientific theories cannot be proven. All one can do is accumulate supportive evidence and disprove alternative hypotheses, which, indeed, is what Lavoisier spent the next two decades doing, right up to the day of his execution on the guillotine in 1794, which is surely one of the most shocking and unjust ends to befall one of the world’s greatest scientists. For while he may not have discovered either of the two gases to which he gave names, what he did was something far more fundamental and far-reaching. For he gave the world the first two building blocks of a whole new chemistry: one in which the entire material universe would come to be seen as assembled out of a finite number of elemental constituents, chemically bonded in different combinations to produce different substances. And it was this that was his real achievement. Not the discovery of any particular gas. For, in the strictest sense, he never discovered anything at all. What he did, was create a whole new way of conceiving of the material world – much like Einstein – altering our conceptual framework in a way that also has implications for the nature of science itself. For instead of progressing in the manner of a steady, incremental accumulation of knowledge – which is how science is so often represented, especially by scientists themselves – it would appear from the examples of both Einstein and Lavoisier that, occasionally, a science will completely renew itself, throwing away the old and starting again on the basis of whole new paradigm.

Even more astonishingly, historical evidence would suggest that such paradigm shifts, as Thomas Kuhn called them, are far more commonplace than one might imagine, their periodic occurrence being made almost inevitable by the unprovable nature of scientific theories combined with our own stubborn reluctance to abandon established theories – no matter how full of holes they may be – until someone has come up with something better: the inevitable result being that, within any given field of science, problems tend to build up until, eventually, the damn bursts.

In his seminal work, ‘The Structure of Scientific Revolutions’, Kuhn, in fact, describes how this usually comes about, outlining the typical lifecycle of a scientific theory from its inception, through its maturity, to its eventual demise. Unsurprisingly, he describes how the introduction of a new theory is almost invariably met by fierce resistance from the existing scientific establishment, which, by dint of simply being established, usually has a lifetime of investment in the previous theory. Indeed, we have already seen an instance of this in the case of Joseph Priestly and other diehard phlogistic chemists, who resisted Lavoisier’s new-fangled ideas long after it was reasonable to do so. What this also means is that the early-adopters of any new theory tend to be younger scientists who have yet to make a reputation for themselves, have nothing to lose, and are excited by the prospect that, by adopting these revolutionary new ideas, they may be the ones to finally solve the many outstanding problems which their science has accumulated over the years.

As take up of the new paradigm increases, however, it eventually begins to raise as many questions as it answers. For reality is invariably richer and more complicated than we initially conceive it to be, such that the more we study it, the more questions it poses. And while some of these questions may lead to new discoveries – thereby raising the level of excitement still further – a body of trickier, more stubborn questions inevitably starts to accumulate, creating problems which can begin to seem almost as intractable as those which beset the old paradigm. In fact, it is not unusual for a new paradigm to even reinstate problems which the old paradigm had actually solved. With no new theory as yet on the horizon, however, a whole new generation of scientists now finds itself in much the same position as their predecessors, having to conjure up additional, supplementary or subordinate theories in order to explain the anomalies and exceptions for which the main theory cannot account.

The problem with this, however, is that every supplementary theory that is needed to bolster the main theory effectively weakens the paradigm, which, in most cases, will have been conceived and initially embraced because it promised to make everything simpler. Now, the whole thing has become a complete mess, with ad hoc fixes all over the place, making the entire science ripe for someone to finally come along and say: ‘You know what, we’ve been looking at this in completely the wrong way. Instead of thinking of it like this, we should think of it like this.’ And thus a new paradigm is born. And the whole cycle starts all over again.

Needless to say, most scientists don’t much like this model of how science works. They much prefer the paradigm in which science is seen as a steady accumulation of knowledge, to which each contribution is equally valid and of equal value. For what is most disturbing about Thomas Kuhn’s revolutionary vision to most scientists is not just the idea that any scientist could wake up tomorrow morning to find their whole life’s work invalidated – rendering them as ridiculous and irrelevant as Joseph Priestly – but that science might actually demand of them something more than a mere journeyman’s contribution to a collective effort: that it might, indeed, demand of some – those who are to be deemed great – an individual leap of genius of the kind Immanuel Kant described in the ‘Critique of Judgement’, where ‘genius’ is defined not as mere cleverness – however extraordinary such cleverness may be – but as the possession of precisely this rare ability to get others to see the world in a new and different way, whether this be in the visual arts, philosophy of the type which Kant himself wrote, or, indeed, science.

In defense of their preferred paradigm, therefore, most scientists will almost certainly argue that, while such revolutionary paradigm shifts may have happened in the past – the historical evidence for their occurrence, from Lavoisier to Einstein, being undeniable – because science proceeds by eliminating false theories, it follows that their frequency will naturally decline  over time – as more and more false theories are removed – until, eventually, they cease altogether, a point we may already have reached.

This argument, however, is based on a belief in what is known as convergence – between our scientific or theoretical conception of the universe and the reality of the universe as it exists in itself – and contains two main flaws. The first is the assumption that, as we replace old, falsified theories with new theories, these new theories will necessarily be ‘true’ in the sense of corresponding to reality. Because all scientific theories are constructs of the imagination, however, there is actually no good reason to believe this, in that we could simply go on continually replacing one false theory with another, which, in time, also turns out to be false.

The second flaw in the argument, however, is even more significant. For even if the above assumption were correct and we gradually replaced all false theories with ‘true’ ones, such that eventually we ended up with a perfect correspondence between our theoretical conception of the universe and the reality of the universe as it exists in itself, we could never know this was the case. For in that it is only through our theoretical conception of the universe that we are able to apprehend it, the one thing we cannot do is step outside of ourselves and compare that theoretical conception with the ‘real’ thing to see whether they correspond. Indeed, the only indication we could have that correspondence had been reached would be if scientists suddenly ran out of questions to ask. And even then we wouldn’t know whether we’d reached correspondence, or whether we’d simply arrived at a set of theories which were perfectly consistent with all the empirical evidence.

To those unfamiliar with these concepts, this distinction may, of course, seem somewhat strained. If a theory is consistent with all the empirical evidence, how could it not correspond to reality? As an exercise in clarification, therefore, ask yourself whether it is possible for two competing scientific theories to both be fully consistent with all the currently available empirical evidence. Assuming that you answer this question in the affirmative, now ask yourself whether it’s possible for both of these theories to be true in the sense of correspondence. If the answer to this question is ‘No’, then this means that at least one of the two theories, both of which are fully consistent with all the empirical evidence, cannot logically correspond to reality. And if it’s possible for one of the theories to be fully consistent with all the empirical evidence and still not correspond to reality, then it follows that it is possible for both theories to fall into this category. Indeed, it’s possible for all our scientific theories to be fully consistent with all the empirical evidence and yet for none of them to correspond to the way the universe actually is in itself.

What this teaches us, however, is not that science is somehow defective, but rather the importance, not just of knowing which criterion of ‘truth’ – ‘consistency’ or ‘correspondence’ – is applicable in any particular context, but of ensuring that where only the weaker criterion of consistency applies, as in the case of a scientific theory, the scientific theory in question is properly grounded in those basic elements of science, namely observation and measurement, to which the stronger criterion of correspondence is applicable. For while we may never be able know whether our scientific theories correspond to reality, we can certainly find out whether our measurements do. And while this may be stating the obvious, sometimes the obvious needs to be stated: that the integrity of scientific theories depends on them being grounded in empirical evidence. For it is this that prevents them from simply coming adrift and floating away on flights of imaginative fancy, which is precisely what can happen in the case of computer simulations.

This is because, in building such simulations, scientists have a tendency to make three fundamental errors, two of which we have more or less already covered. The first of these is the mistaken belief that, if a theory accurately predicts future events then it must be true, even though there have been numerous scientific theories throughout history with a high degree of predictive accuracy which have ultimately turned out to be false. The second mistake is then to take the ‘truth’ of these theories to mean ‘correspondence’, which then opens the door to two further conceptual errors. For not only does the belief in a theory’s correspondence to reality remove any threat that the theory might one day be overturned in yet another scientific revolution – thereby reducing the level of caution with which scientists might otherwise regard the reliability of any computer model based on it – but it actually elevates the theory to the status of something absolute and immutable, thereby reducing the perceived need to validate it empirically.

Indeed, we see this in the way in which many computer models are developed, in a continuous process which starts by running the model against historical data and adjusting the algorithm until its output approximately matches the data record. It’s a bit like running ‘Goal Seek’ in Excel. You know what answers you want; so you just keep tweaking the computational engine until you get them. The trouble with this, however, is that it also effectively refines the theoretical construct upon which the algorithm is based. And while every effort is usually made to empirically validate these changes to the underlying theory, such that they are not just random and have some basis in the real world, the verification process – the matching of the model’s output to real world data – takes precedence, such that once data congruence is achieved, the validity of the model, as an accurate representation of the world, is seen as less important. Indeed, the mere fact that the output now corresponds to the historical record is taken as evidence that the modified model is correct.

This tendency to implicitly favour verification over validation is further emphasised in large multiscale simulations, where the outputs from smaller, lower level models are fed into a larger, higher level models as mathematical parameters, which are regularly tweaked without validation in order to ensure that the outputs from the top level model correspond to the real world. This process, known as parameterization, is especially prevalent is cases where the lower levels systems being modelled are inherently chaotic and are only statistically determinate at the macro level, thereby seeming to justify the lack of validation.

Nor are these the only ways in which computer simulations tend towards artifice rather than real world representation. For in a manner very similar to Thomas Kuhn’s description of how supplementary, ad hoc theories are used to explain exceptions to a main theory during the mature phase of scientific paradigm’s lifecycle, so too supplementary, ad hoc programs are often written into computer models simply in order to ‘make the model work’, whether or not these supplementary programs have any real world correlation. The result, as described by Eric Winsberg in his essay ‘Computer Simulations In Science’, is that the outputs from computer models:

typically depend not just on theory but on many other model ingredients and resources as well, including parameterizations (discussed above), numerical solution methods, mathematical tricks, approximations and idealizations, outright fictions, ad hoc assumptions… and perhaps most importantly, the blood, sweat, and tears of much trial and error.

Indeed, some large, multiscale computer simulations take years – even decades – to develop, and are less built than grown organically, which can give rise to its own set of problems, especially in a university setting where numerous generations of post-graduate students may have worked on the simulation, each making their own modifications without always adequately documenting them, such that eventually it becomes very difficult to say how the model actually works, or even what it represents, rendering the idea that it could somehow correspond to reality not just laughable but conceptually confused.

Indeed, we have seen an example of this very recently in the case of the epidemiological computer model developed by Professor Neil Ferguson and his team at Imperial College London, which predicted that over half a million people in the UK could die of Covid-19, thereby forcing the government to impose draconian lockdown measures. Not only has this prediction turned out to have been massively awry, however, but it has since been discovered that the sixteen year old code from which it was generated has been so frequently altered during its lifetime that large parts of it are more or less unintelligible to any computer programmer trying to work out what they do. In short, the whole program has effectively become a ‘black box’: a mystical engine for issuing predictions, some of which turn out to be correct, though nobody knows how or why.

This is not just bad science; it is no longer science at all. It’s more like the Oracle at Delphi, which, of course, is exactly what governments really want from science: not the pure and disinterested kind of science which merely seeks to describe and explain how the universe works, but a shaman’s tool which they can use to take the uncertainty and political exposure out of decision-making. For this, of course, is how it has always been. In the past, rulers consulted priests who read the auguries for them. Now they consult their scientific advisers: a modern alternative which has the added benefit of allowing them to say that they are simply following scientific advice, thus absolving them of any responsibility for anything that might go wrong should the auguries turn out to be false.

Thus, while it is likely that a number of individual scientists will eventually be made scapegoats for the catastrophic economic consequences which will undoubtedly follow from the mishandling of the coronavirus pandemic by governments around the world, the biggest casualty resulting from this folly is almost certain to be science itself. For while science, in the abstract, may not be responsible for the fact that so many of those who practice it don’t really understand it and – not knowing what they do – are therefore willing to mangle and corrupt it for their own self-advancement, it is doubtful whether those whose lives are devastated by its misuse will make such a fine distinction. Seeing only scientists to blame, they will likely blame science too, thus almost certainly undermining one of the most important pillar upon which our civilization has been built, much to the detriment of us all.